Projects I and II address the need for structural genomics projects to have access to improved alignment algorithms for structure-structure, sequence-structure, and sequence-sequence analysis. Collectively, these methods will contribute to: (i) target selection, which sequences are most likely to provide a fold not seen before; (ii) functional annotation through recognition of similarities to existing correctly annotated sequences of structures; (iii) provision of useful structural models from sequences where no exceptional structure is available. Alignments in Project I are based on statistical mechanical models. This algorithm uses recursion relationships developed from a partition function formulation of alignment probabilities. In the case of structure- structure alignments, the algorithm uses simple partition functions from polymer physics and essentially provides a physical theory of structural alignment. It is implemented within a dynamic programming format that closely resembles the "forward algorithm" commonly used in Hidden Markov alignment path, and will be referred to as the SDP algorithm. Alignments from Project II are based upon a Bayesian network model combined with the three-dimensional profile approach. The approach will lead to an alternative classification of protein folds and superfamilies that incorporate both primary sequences and structural information useful in recognition of remote homologs for which structures already exist.